code: def test(loader, model, device="cuda"):
      model.eval()
  
      correct = 0
      num_graphs = 0
      for batch in loader:
          batch.to(device)
          with torch.no_grad():
              pred = model(batch).max(dim=1)[1]
              label = batch.graph_label
          correct += pred.eq(label).sum().item()
          num_graphs += batch.num_graphs
      return correct / num_graphs
  
imports: import torch
  import argparse
  import skip_models
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import TUDataset
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 4 for calling a global function with example usage: test(loader, model, device) and returns: unknown